Goal¶

In this project, I aim at finding the likelihood of a customer leaving the organisation, the key indicators of churn as well as the retention strategies that can be implemented to prevent this problem.¶

Questions¶

Q1. Which customer Internet service packages churn the most?¶

Q2. Do Males churn more than females?¶

Q3. Do dependent customers churn more? If yes, why?¶

Q4. Do contract types affect churn rate ?¶

Q5. Does tenure influence the retention or churn rate of customers?¶¶

Q6. Do customers with Partners Churn more than customers without partners?¶¶

Hypothesis¶

1. Males receive better services hence churn less¶

2. Customers with the fibre optic internet service package churn the most¶

In [1]:
## Importing relevant Libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt 
!pip install seaborn 
import seaborn as sns
import plotly.graph_objects as go
import warnings
from sklearn.linear_model import SGDClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_squared_log_error
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.metrics import *
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.metrics import fbeta_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import MinMaxScaler
from statsmodels.stats.outliers_influence import variance_inflation_factor
from imblearn.over_sampling import SMOTE
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import *
import plotly.express as px
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import make_column_transformer

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In [2]:
#Loaading dataset 
Customer_churn = pd.read_csv('/Users/Admin/Desktop/Telco-Customer-Churn.csv')
Customer_churn.head()
Out[2]:
customerID gender SeniorCitizen Partner Dependents tenure PhoneService MultipleLines InternetService OnlineSecurity ... DeviceProtection TechSupport StreamingTV StreamingMovies Contract PaperlessBilling PaymentMethod MonthlyCharges TotalCharges Churn
0 7590-VHVEG Female 0 Yes No 1 No No phone service DSL No ... No No No No Month-to-month Yes Electronic check 29.85 29.85 No
1 5575-GNVDE Male 0 No No 34 Yes No DSL Yes ... Yes No No No One year No Mailed check 56.95 1889.5 No
2 3668-QPYBK Male 0 No No 2 Yes No DSL Yes ... No No No No Month-to-month Yes Mailed check 53.85 108.15 Yes
3 7795-CFOCW Male 0 No No 45 No No phone service DSL Yes ... Yes Yes No No One year No Bank transfer (automatic) 42.30 1840.75 No
4 9237-HQITU Female 0 No No 2 Yes No Fiber optic No ... No No No No Month-to-month Yes Electronic check 70.70 151.65 Yes

5 rows × 21 columns

In [3]:
Customer_churn.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   gender            7043 non-null   object 
 2   SeniorCitizen     7043 non-null   int64  
 3   Partner           7043 non-null   object 
 4   Dependents        7043 non-null   object 
 5   tenure            7043 non-null   int64  
 6   PhoneService      7043 non-null   object 
 7   MultipleLines     7043 non-null   object 
 8   InternetService   7043 non-null   object 
 9   OnlineSecurity    7043 non-null   object 
 10  OnlineBackup      7043 non-null   object 
 11  DeviceProtection  7043 non-null   object 
 12  TechSupport       7043 non-null   object 
 13  StreamingTV       7043 non-null   object 
 14  StreamingMovies   7043 non-null   object 
 15  Contract          7043 non-null   object 
 16  PaperlessBilling  7043 non-null   object 
 17  PaymentMethod     7043 non-null   object 
 18  MonthlyCharges    7043 non-null   float64
 19  TotalCharges      7043 non-null   object 
 20  Churn             7043 non-null   object 
dtypes: float64(1), int64(2), object(18)
memory usage: 1.1+ MB
In [4]:
## Viewing dataset
Customer_churn
Out[4]:
customerID gender SeniorCitizen Partner Dependents tenure PhoneService MultipleLines InternetService OnlineSecurity ... DeviceProtection TechSupport StreamingTV StreamingMovies Contract PaperlessBilling PaymentMethod MonthlyCharges TotalCharges Churn
0 7590-VHVEG Female 0 Yes No 1 No No phone service DSL No ... No No No No Month-to-month Yes Electronic check 29.85 29.85 No
1 5575-GNVDE Male 0 No No 34 Yes No DSL Yes ... Yes No No No One year No Mailed check 56.95 1889.5 No
2 3668-QPYBK Male 0 No No 2 Yes No DSL Yes ... No No No No Month-to-month Yes Mailed check 53.85 108.15 Yes
3 7795-CFOCW Male 0 No No 45 No No phone service DSL Yes ... Yes Yes No No One year No Bank transfer (automatic) 42.30 1840.75 No
4 9237-HQITU Female 0 No No 2 Yes No Fiber optic No ... No No No No Month-to-month Yes Electronic check 70.70 151.65 Yes
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
7038 6840-RESVB Male 0 Yes Yes 24 Yes Yes DSL Yes ... Yes Yes Yes Yes One year Yes Mailed check 84.80 1990.5 No
7039 2234-XADUH Female 0 Yes Yes 72 Yes Yes Fiber optic No ... Yes No Yes Yes One year Yes Credit card (automatic) 103.20 7362.9 No
7040 4801-JZAZL Female 0 Yes Yes 11 No No phone service DSL Yes ... No No No No Month-to-month Yes Electronic check 29.60 346.45 No
7041 8361-LTMKD Male 1 Yes No 4 Yes Yes Fiber optic No ... No No No No Month-to-month Yes Mailed check 74.40 306.6 Yes
7042 3186-AJIEK Male 0 No No 66 Yes No Fiber optic Yes ... Yes Yes Yes Yes Two year Yes Bank transfer (automatic) 105.65 6844.5 No

7043 rows × 21 columns

In [5]:
### Dropping the customer ID colums because it is irrelevant to my analysis
Customer_churn.drop(columns = ['customerID'], inplace = True)
Customer_churn
Out[5]:
gender SeniorCitizen Partner Dependents tenure PhoneService MultipleLines InternetService OnlineSecurity OnlineBackup DeviceProtection TechSupport StreamingTV StreamingMovies Contract PaperlessBilling PaymentMethod MonthlyCharges TotalCharges Churn
0 Female 0 Yes No 1 No No phone service DSL No Yes No No No No Month-to-month Yes Electronic check 29.85 29.85 No
1 Male 0 No No 34 Yes No DSL Yes No Yes No No No One year No Mailed check 56.95 1889.5 No
2 Male 0 No No 2 Yes No DSL Yes Yes No No No No Month-to-month Yes Mailed check 53.85 108.15 Yes
3 Male 0 No No 45 No No phone service DSL Yes No Yes Yes No No One year No Bank transfer (automatic) 42.30 1840.75 No
4 Female 0 No No 2 Yes No Fiber optic No No No No No No Month-to-month Yes Electronic check 70.70 151.65 Yes
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
7038 Male 0 Yes Yes 24 Yes Yes DSL Yes No Yes Yes Yes Yes One year Yes Mailed check 84.80 1990.5 No
7039 Female 0 Yes Yes 72 Yes Yes Fiber optic No Yes Yes No Yes Yes One year Yes Credit card (automatic) 103.20 7362.9 No
7040 Female 0 Yes Yes 11 No No phone service DSL Yes No No No No No Month-to-month Yes Electronic check 29.60 346.45 No
7041 Male 1 Yes No 4 Yes Yes Fiber optic No No No No No No Month-to-month Yes Mailed check 74.40 306.6 Yes
7042 Male 0 No No 66 Yes No Fiber optic Yes No Yes Yes Yes Yes Two year Yes Bank transfer (automatic) 105.65 6844.5 No

7043 rows × 20 columns

In [6]:
# changing Totalcharges datatype from object to int/float.
Customer_churn["TotalCharges"] = pd.to_numeric(Customer_churn["TotalCharges"], errors = "coerce").fillna(0, downcast = "infer")
Customer_churn
Out[6]:
gender SeniorCitizen Partner Dependents tenure PhoneService MultipleLines InternetService OnlineSecurity OnlineBackup DeviceProtection TechSupport StreamingTV StreamingMovies Contract PaperlessBilling PaymentMethod MonthlyCharges TotalCharges Churn
0 Female 0 Yes No 1 No No phone service DSL No Yes No No No No Month-to-month Yes Electronic check 29.85 29.85 No
1 Male 0 No No 34 Yes No DSL Yes No Yes No No No One year No Mailed check 56.95 1889.50 No
2 Male 0 No No 2 Yes No DSL Yes Yes No No No No Month-to-month Yes Mailed check 53.85 108.15 Yes
3 Male 0 No No 45 No No phone service DSL Yes No Yes Yes No No One year No Bank transfer (automatic) 42.30 1840.75 No
4 Female 0 No No 2 Yes No Fiber optic No No No No No No Month-to-month Yes Electronic check 70.70 151.65 Yes
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
7038 Male 0 Yes Yes 24 Yes Yes DSL Yes No Yes Yes Yes Yes One year Yes Mailed check 84.80 1990.50 No
7039 Female 0 Yes Yes 72 Yes Yes Fiber optic No Yes Yes No Yes Yes One year Yes Credit card (automatic) 103.20 7362.90 No
7040 Female 0 Yes Yes 11 No No phone service DSL Yes No No No No No Month-to-month Yes Electronic check 29.60 346.45 No
7041 Male 1 Yes No 4 Yes Yes Fiber optic No No No No No No Month-to-month Yes Mailed check 74.40 306.60 Yes
7042 Male 0 No No 66 Yes No Fiber optic Yes No Yes Yes Yes Yes Two year Yes Bank transfer (automatic) 105.65 6844.50 No

7043 rows × 20 columns

In [7]:
Customer_churn.drop(Customer_churn[Customer_churn['TotalCharges'] == 0].index,inplace = True)
Customer_churn.reset_index(drop = True, inplace = True)
Customer_churn
Out[7]:
gender SeniorCitizen Partner Dependents tenure PhoneService MultipleLines InternetService OnlineSecurity OnlineBackup DeviceProtection TechSupport StreamingTV StreamingMovies Contract PaperlessBilling PaymentMethod MonthlyCharges TotalCharges Churn
0 Female 0 Yes No 1 No No phone service DSL No Yes No No No No Month-to-month Yes Electronic check 29.85 29.85 No
1 Male 0 No No 34 Yes No DSL Yes No Yes No No No One year No Mailed check 56.95 1889.50 No
2 Male 0 No No 2 Yes No DSL Yes Yes No No No No Month-to-month Yes Mailed check 53.85 108.15 Yes
3 Male 0 No No 45 No No phone service DSL Yes No Yes Yes No No One year No Bank transfer (automatic) 42.30 1840.75 No
4 Female 0 No No 2 Yes No Fiber optic No No No No No No Month-to-month Yes Electronic check 70.70 151.65 Yes
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
7027 Male 0 Yes Yes 24 Yes Yes DSL Yes No Yes Yes Yes Yes One year Yes Mailed check 84.80 1990.50 No
7028 Female 0 Yes Yes 72 Yes Yes Fiber optic No Yes Yes No Yes Yes One year Yes Credit card (automatic) 103.20 7362.90 No
7029 Female 0 Yes Yes 11 No No phone service DSL Yes No No No No No Month-to-month Yes Electronic check 29.60 346.45 No
7030 Male 1 Yes No 4 Yes Yes Fiber optic No No No No No No Month-to-month Yes Mailed check 74.40 306.60 Yes
7031 Male 0 No No 66 Yes No Fiber optic Yes No Yes Yes Yes Yes Two year Yes Bank transfer (automatic) 105.65 6844.50 No

7032 rows × 20 columns

In [8]:
Customer_churn.isnull().sum()
Out[8]:
gender              0
SeniorCitizen       0
Partner             0
Dependents          0
tenure              0
PhoneService        0
MultipleLines       0
InternetService     0
OnlineSecurity      0
OnlineBackup        0
DeviceProtection    0
TechSupport         0
StreamingTV         0
StreamingMovies     0
Contract            0
PaperlessBilling    0
PaymentMethod       0
MonthlyCharges      0
TotalCharges        0
Churn               0
dtype: int64
In [9]:
Customer_churn['TotalCharges']
Out[9]:
0         29.85
1       1889.50
2        108.15
3       1840.75
4        151.65
         ...   
7027    1990.50
7028    7362.90
7029     346.45
7030     306.60
7031    6844.50
Name: TotalCharges, Length: 7032, dtype: float64
In [10]:
!pip install pandas-profiling

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In [11]:
from pandas_profiling import ProfileReport
Customer_churn.profile_report()

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]
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Out[11]:




























EDA¶


















In [12]:



     


Customer_churn.corr()



     






















    
    
Out[12]:











  
    

      
      SeniorCitizen
      tenure
      MonthlyCharges
      TotalCharges
    

  
  
    

      SeniorCitizen
      1.000000
      0.015683
      0.219874
      0.102411
    

    

      tenure
      0.015683
      1.000000
      0.246862
      0.825880
    

    

      MonthlyCharges
      0.219874
      0.246862
      1.000000
      0.651065
    

    

      TotalCharges
      0.102411
      0.825880
      0.651065
      1.000000
    

  



























In [13]:



     


sns.set(rc = {'figure.figsize':(9,9)})
sns.heatmap(Customer_churn.corr(), annot =True)



     






















    
    
Out[13]:








<AxesSubplot:>










    
    



































Visualizations¶






















Q1. Which customer Internet service packages churn the most?¶


















In [14]:



     


# Checking the number of customers on different internet packages.
Internet_package = Customer_churn['InternetService'].value_counts()
Result = pd.DataFrame(Internet_package)

# Creating columns for new datafram called results.
Result = Result.reset_index()
Result.columns = ['InternetService', 'Number_of_customers']
Result



     






















    
    
Out[14]:











  
    

      
      InternetService
      Number_of_customers
    

  
  
    

      0
      Fiber optic
      3096
    

    

      1
      DSL
      2416
    

    

      2
      No
      1520
    

  































As shown in the table above, most customers prefer the Fiber optic InternetService to the others.¶


















In [15]:



     


# Plotting a graph to show the percentage of customer churn per InternetService 
fig1 = go.Figure(
    data=go.Pie(values=Customer_churn['InternetService'].value_counts().values,labels=Customer_churn['InternetService'].value_counts().index,title='Percentage of customer churn for Internet services'))
fig1.show()



     






















    
    






        
        









    
    







                            
                    




























From the pie chart it is evident that customers churn the most with the Fiber optic Internet service. This is a bit off because most customers prefer the Fiber optic Internet Service package. The graphs below are going to explain why customers who patronise the Fiber optic Internet Service Package churn the most.¶


















In [16]:



     


# Matching the internet service with the tech support they receive.
sns.set(rc = {'figure.figsize': (10,6)})
sns.countplot(data = Customer_churn, x= 'InternetService', hue = 'TechSupport', palette = 'inferno_r')



     






















    
    
Out[16]:








<AxesSubplot:xlabel='InternetService', ylabel='count'>










    
    































In [17]:



     


## Plotting a graph to visualize the level of online security received by various Internet services 
sns.set(rc = {'figure.figsize': (10,6)})
sns.countplot(data = Customer_churn, x= 'InternetService', hue = 'OnlineSecurity', palette = 'inferno_r')



     






















    
    
Out[17]:








<AxesSubplot:xlabel='InternetService', ylabel='count'>










    
    



































From further analysis, Fibre Optic customers seem to churn most because they tend not to receive a lot of online security. It could also be that customers opt in for the Fiber Optic internet Service package and later find out that the online security is minimal, hence the high level of churn. It could also be due to the low level of tech support or even high monthly charges.¶






















Q2. Do Males churn more than females?¶


















In [18]:



     


sns.set(rc = {'figure.figsize': (10,6)})
sns.countplot(data = Customer_churn, x= 'gender', hue = 'Churn', palette = 'twilight')



     






















    
    
Out[18]:








<AxesSubplot:xlabel='gender', ylabel='count'>










    
    



































From the graph, we can see that females churn slightly higher than males. This may be as a result of various factors. It could be the rate of monthly charges, the contract specifications, the type of internet service package  etc.¶






















Q3. Do dependent customers churn more? If yes, why?¶


















In [19]:



     


# Plotting a count plot to visualize the churn amount of dependents
sns.set(rc = {'figure.figsize': (10,6)})
sns.countplot(data = Customer_churn, x= 'Dependents', hue = 'Churn', palette = 'Greens_r')



     






















    
    
Out[19]:








<AxesSubplot:xlabel='Dependents', ylabel='count'>










    
    































In [20]:



     


# Plotting a count plot to visualize the churn amount of dependents
ax = sns.countplot(data= Customer_churn, x='Dependents', hue='Churn',palette="pastel")
for container in ax.containers:
    ax.bar_label(container)



     






















    
    



































From the graaph above, we can deduce that dependents churn way less than non-dependents. This may be as a result of various factors. We can see from the graph above that the churn amount of non dependents is 1543 and that of dependents is 326.¶






















Q4. Do contract types affect churn rate ?¶






















Firstly, I am going to check the various forms of contracts available with the Telco and the number of customers associated with each contract type.¶


















In [21]:



     


# Checking the number of customers associated with each contract type
Contract_type = Customer_churn['Contract'].value_counts()
Contract_results = pd.DataFrame(Contract_type)

# creating columns for new dataframe called Contract_results.
Contract_results = Contract_results.reset_index()
Contract_results.columns = ['Contract', 'Total_customers']
Contract_results



     






















    
    
Out[21]:











  
    

      
      Contract
      Total_customers
    

  
  
    

      0
      Month-to-month
      3875
    

    

      1
      Two year
      1685
    

    

      2
      One year
      1472
    

  































We can see that there are three types of contracts with the Month-to-month having the most customers. Next we are going to check the churn rate for all three contract types using a count plot.¶


















In [22]:



     


# Matching the Contract type with the churn rate 
sns.set(rc = {'figure.figsize': (10,5)})
sns.countplot(data = Customer_churn, x= 'Contract', hue = 'Churn', palette = 'pastel')



     






















    
    
Out[22]:








<AxesSubplot:xlabel='Contract', ylabel='count'>










    
    































In [23]:



     


# Matching the contract type with the InternetService patronised
sns.set(rc = {'figure.figsize': (10,5)})
sns.countplot(data = Customer_churn, x= 'Contract', hue = 'InternetService', palette = 'magma')



     






















    
    
Out[23]:








<AxesSubplot:xlabel='Contract', ylabel='count'>










    
    



































From my analysis, it is clear that the Month-to-month contract types churn the most. The second countplot compares the contract type to the Internet service patronised by customers.  It makes perfect sense that the month-to-month contract churns the most because most of the customers on this contract also use more of the Fiber Optic internet package. And as shown above, cutomers on the fiber optic internet service tend to churn a lot due to several factors like, minimal online security, no tech support, high monthly charges etc.¶






















Q5. Does tenure influence the retention or churn rate of customers?¶


















In [24]:



     


# Boxplot showing the impact of tenure on the churn rate 
sns.boxplot(data = Customer_churn, x = 'tenure', y = 'Churn', palette = 'ocean')            



     






















    
    
Out[24]:








<AxesSubplot:xlabel='tenure', ylabel='Churn'>










    
    



































Most of the customers are within the first (1 - 29 months) and third quartiles (29 - 55 months), with a significant number being in the area above the median. From the boxplots above, customers who passed the 29-month mark through to the 60th mark generally churned less. Most of the churned customers are from month 1 to 29. With majority of the customers falling within the 1-29 month group, it is just as understandable as it is concerning the churn levels are high. It makes sense because customers within this period may come in and leave at anytime. This is the period in which we have new customers coming in. It is also concerning because it poses a question of effectiveness on the retention strategy of the "new customers".¶











































Q6. Do customers with Partners Churn more than customers without partners?¶


















In [25]:



     


# Plotting a graph to compare the churn rate of customers with partners and those with no partners. 
sns.set(rc = {'figure.figsize': (10,5)})
sns.countplot(data = Customer_churn, x= 'Partner', hue = 'Churn', palette = 'magma')



     






















    
    
Out[25]:








<AxesSubplot:xlabel='Partner', ylabel='count'>










    
    



































We can conclude from the visualization above that, customers with no partners tend to churn the most. This may be as a result of various reasons which may include the cost of charges per month. It would make perfect sense to assume that customers with partners may share costs in settling charges for services per month, hence the burden of paying for services is lessened. On the other hand, customers with no partners have all the burden of paying for services on their head. If monthly charges are high, it is only right for customers with no partners to churn.¶






















Hypothesis¶






















1.  Males receive better services hence churn less than females¶


















In [26]:



     


# Visualizing the churn rate of Males and females
ax = sns.countplot(data= Customer_churn, x='gender', hue='Churn',palette="ocean")
for container in ax.containers:
    ax.bar_label(container)



     






















    
    































In [124]:



     


# Matching the device protection service against gender
ax = sns.countplot(data= Customer_churn, x='gender', hue='DeviceProtection',palette="twilight")
sns.set(rc = {'figure.figsize': (15,10)})
for container in ax.containers:
    ax.bar_label(container)



     






















    
    



































From the plot above, Males receive higher device protection services from the Telco, hence they churn less. This satisfies the hypothesis stated above.¶


















In [28]:



     


# Matching the tech support received against gender
ax = sns.countplot(data= Customer_churn, x='gender', hue='TechSupport',palette="twilight")
sns.set(rc = {'figure.figsize': (15,10)})
for container in ax.containers:
    ax.bar_label(container)



     






















    
    



































Another interesting thing happens when we match the TechSupport received against gender. We can see that, here females receive slightly higher tech support than males do. This does not satisfy the hypothesis stated above.¶
We can conclude that, the reason why females churn more than males is not solely due to services received.¶






















2. Customers with the fibre optic internet service package churn the most¶


















In [29]:



     


sns.set(rc = {'figure.figsize': (10,6)})
sns.countplot(data = Customer_churn, x= 'InternetService', hue = 'Churn', palette = 'winter_r')



     






















    
    
Out[29]:








<AxesSubplot:xlabel='InternetService', ylabel='count'>










    
    



































It is clearly evident from the countplot above that the Fiber Optic InternetService churns the most. The various reasons for the high churn rate are already stated above. We can conclude that the stated hypothesis holds.¶


















In [30]:



     


# Getting a concise summary of dataset 
Customer_churn.info()



     






















    
    






<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7032 entries, 0 to 7031
Data columns (total 20 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   gender            7032 non-null   object 
 1   SeniorCitizen     7032 non-null   int64  
 2   Partner           7032 non-null   object 
 3   Dependents        7032 non-null   object 
 4   tenure            7032 non-null   int64  
 5   PhoneService      7032 non-null   object 
 6   MultipleLines     7032 non-null   object 
 7   InternetService   7032 non-null   object 
 8   OnlineSecurity    7032 non-null   object 
 9   OnlineBackup      7032 non-null   object 
 10  DeviceProtection  7032 non-null   object 
 11  TechSupport       7032 non-null   object 
 12  StreamingTV       7032 non-null   object 
 13  StreamingMovies   7032 non-null   object 
 14  Contract          7032 non-null   object 
 15  PaperlessBilling  7032 non-null   object 
 16  PaymentMethod     7032 non-null   object 
 17  MonthlyCharges    7032 non-null   float64
 18  TotalCharges      7032 non-null   float64
 19  Churn             7032 non-null   object 
dtypes: float64(2), int64(2), object(16)
memory usage: 1.1+ MB




























Feature Engineering¶


















In [31]:



     


# checking for unique vales for dataset 
Customer_churn.nunique()



     






















    
    
Out[31]:








gender                 2
SeniorCitizen          2
Partner                2
Dependents             2
tenure                72
PhoneService           2
MultipleLines          3
InternetService        3
OnlineSecurity         3
OnlineBackup           3
DeviceProtection       3
TechSupport            3
StreamingTV            3
StreamingMovies        3
Contract               3
PaperlessBilling       2
PaymentMethod          4
MonthlyCharges      1584
TotalCharges        6530
Churn                  2
dtype: int64
























In [32]:



     


# Dropping the numerical columns in the Customer churn dataset. 
sample_df = Customer_churn.drop(['MonthlyCharges','TotalCharges','tenure'],axis=1)



     



















In [33]:



     


# Creating a dataframe called encoded to apply One hot encoding to categorical data columns
Encoded = OneHotEncoder(sparse=False, drop='first',
    handle_unknown='ignore')



     



















In [34]:



     


# Encoding categorical columns
tfd = make_column_transformer((Encoded, ['gender','Partner','Dependents','PhoneService','MultipleLines','InternetService','OnlineSecurity','OnlineBackup','DeviceProtection','TechSupport','StreamingTV','StreamingMovies','Contract','PaperlessBilling','PaymentMethod']),remainder='passthrough',)



     



















In [35]:



     


# Assigning categorical data to a single dataframe called categoricals
categoricals = ['gender','Partner','Dependents','PhoneService','MultipleLines','InternetService',
                'OnlineSecurity','OnlineBackup','DeviceProtection','TechSupport','StreamingTV',
                'StreamingMovies','Contract','PaperlessBilling','PaymentMethod']



     



















In [36]:



     


Customer_churn_encoded = tfd.fit_transform(sample_df)
Customer_churn_encoded = pd.DataFrame(Customer_churn_encoded, columns=tfd.get_feature_names_out())



     



















In [37]:



     


# Viewing the encoded dataset
Customer_churn_encoded



     






















    
    
Out[37]:











  
    

      
      onehotencoder__gender_Male
      onehotencoder__Partner_Yes
      onehotencoder__Dependents_Yes
      onehotencoder__PhoneService_Yes
      onehotencoder__MultipleLines_No phone service
      onehotencoder__MultipleLines_Yes
      onehotencoder__InternetService_Fiber optic
      onehotencoder__InternetService_No
      onehotencoder__OnlineSecurity_No internet service
      onehotencoder__OnlineSecurity_Yes
      ...
      onehotencoder__StreamingMovies_No internet service
      onehotencoder__StreamingMovies_Yes
      onehotencoder__Contract_One year
      onehotencoder__Contract_Two year
      onehotencoder__PaperlessBilling_Yes
      onehotencoder__PaymentMethod_Credit card (automatic)
      onehotencoder__PaymentMethod_Electronic check
      onehotencoder__PaymentMethod_Mailed check
      remainder__SeniorCitizen
      remainder__Churn
    

  
  
    

      0
      0.0
      1.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
      0.0
      ...
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      1.0
      0.0
      0
      No
    

    

      1
      1.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
      0.0
      1.0
      ...
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
      1.0
      0
      No
    

    

      2
      1.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
      0.0
      1.0
      ...
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      0.0
      1.0
      0
      Yes
    

    

      3
      1.0
      0.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
      1.0
      ...
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0
      No
    

    

      4
      0.0
      0.0
      0.0
      1.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      ...
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      1.0
      0.0
      0
      Yes
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      7027
      1.0
      1.0
      1.0
      1.0
      0.0
      1.0
      0.0
      0.0
      0.0
      1.0
      ...
      0.0
      1.0
      1.0
      0.0
      1.0
      0.0
      0.0
      1.0
      0
      No
    

    

      7028
      0.0
      1.0
      1.0
      1.0
      0.0
      1.0
      1.0
      0.0
      0.0
      0.0
      ...
      0.0
      1.0
      1.0
      0.0
      1.0
      1.0
      0.0
      0.0
      0
      No
    

    

      7029
      0.0
      1.0
      1.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
      1.0
      ...
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      1.0
      0.0
      0
      No
    

    

      7030
      1.0
      1.0
      0.0
      1.0
      0.0
      1.0
      1.0
      0.0
      0.0
      0.0
      ...
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      0.0
      1.0
      1
      Yes
    

    

      7031
      1.0
      0.0
      0.0
      1.0
      0.0
      0.0
      1.0
      0.0
      0.0
      1.0
      ...
      0.0
      1.0
      0.0
      1.0
      1.0
      0.0
      0.0
      0.0
      0
      No
    

  



7032 rows × 28 columns


























In [38]:



     


# Merging the original dataset with the encoded dataset to prepare for modeling
Merged_encoded_data = Customer_churn.join(Customer_churn_encoded)



     



















In [39]:



     


Merged_encoded_data



     






















    
    
Out[39]:











  
    

      
      gender
      SeniorCitizen
      Partner
      Dependents
      tenure
      PhoneService
      MultipleLines
      InternetService
      OnlineSecurity
      OnlineBackup
      ...
      onehotencoder__StreamingMovies_No internet service
      onehotencoder__StreamingMovies_Yes
      onehotencoder__Contract_One year
      onehotencoder__Contract_Two year
      onehotencoder__PaperlessBilling_Yes
      onehotencoder__PaymentMethod_Credit card (automatic)
      onehotencoder__PaymentMethod_Electronic check
      onehotencoder__PaymentMethod_Mailed check
      remainder__SeniorCitizen
      remainder__Churn
    

  
  
    

      0
      Female
      0
      Yes
      No
      1
      No
      No phone service
      DSL
      No
      Yes
      ...
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      1.0
      0.0
      0
      No
    

    

      1
      Male
      0
      No
      No
      34
      Yes
      No
      DSL
      Yes
      No
      ...
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
      1.0
      0
      No
    

    

      2
      Male
      0
      No
      No
      2
      Yes
      No
      DSL
      Yes
      Yes
      ...
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      0.0
      1.0
      0
      Yes
    

    

      3
      Male
      0
      No
      No
      45
      No
      No phone service
      DSL
      Yes
      No
      ...
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0
      No
    

    

      4
      Female
      0
      No
      No
      2
      Yes
      No
      Fiber optic
      No
      No
      ...
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      1.0
      0.0
      0
      Yes
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      7027
      Male
      0
      Yes
      Yes
      24
      Yes
      Yes
      DSL
      Yes
      No
      ...
      0.0
      1.0
      1.0
      0.0
      1.0
      0.0
      0.0
      1.0
      0
      No
    

    

      7028
      Female
      0
      Yes
      Yes
      72
      Yes
      Yes
      Fiber optic
      No
      Yes
      ...
      0.0
      1.0
      1.0
      0.0
      1.0
      1.0
      0.0
      0.0
      0
      No
    

    

      7029
      Female
      0
      Yes
      Yes
      11
      No
      No phone service
      DSL
      Yes
      No
      ...
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      1.0
      0.0
      0
      No
    

    

      7030
      Male
      1
      Yes
      No
      4
      Yes
      Yes
      Fiber optic
      No
      No
      ...
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      0.0
      1.0
      1
      Yes
    

    

      7031
      Male
      0
      No
      No
      66
      Yes
      No
      Fiber optic
      Yes
      No
      ...
      0.0
      1.0
      0.0
      1.0
      1.0
      0.0
      0.0
      0.0
      0
      No
    

  



7032 rows × 48 columns


























In [40]:



     


# Dropping duplicate and old columns
Complete_set = Merged_encoded_data.drop(columns = categoricals)



     



















In [41]:



     


# Viewing the complete set to be used in modeling and prediction 
Complete_set



     






















    
    
Out[41]:











  
    

      
      SeniorCitizen
      tenure
      MonthlyCharges
      TotalCharges
      Churn
      onehotencoder__gender_Male
      onehotencoder__Partner_Yes
      onehotencoder__Dependents_Yes
      onehotencoder__PhoneService_Yes
      onehotencoder__MultipleLines_No phone service
      ...
      onehotencoder__StreamingMovies_No internet service
      onehotencoder__StreamingMovies_Yes
      onehotencoder__Contract_One year
      onehotencoder__Contract_Two year
      onehotencoder__PaperlessBilling_Yes
      onehotencoder__PaymentMethod_Credit card (automatic)
      onehotencoder__PaymentMethod_Electronic check
      onehotencoder__PaymentMethod_Mailed check
      remainder__SeniorCitizen
      remainder__Churn
    

  
  
    

      0
      0
      1
      29.85
      29.85
      No
      0.0
      1.0
      0.0
      0.0
      1.0
      ...
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      1.0
      0.0
      0
      No
    

    

      1
      0
      34
      56.95
      1889.50
      No
      1.0
      0.0
      0.0
      1.0
      0.0
      ...
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
      1.0
      0
      No
    

    

      2
      0
      2
      53.85
      108.15
      Yes
      1.0
      0.0
      0.0
      1.0
      0.0
      ...
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      0.0
      1.0
      0
      Yes
    

    

      3
      0
      45
      42.30
      1840.75
      No
      1.0
      0.0
      0.0
      0.0
      1.0
      ...
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0
      No
    

    

      4
      0
      2
      70.70
      151.65
      Yes
      0.0
      0.0
      0.0
      1.0
      0.0
      ...
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      1.0
      0.0
      0
      Yes
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      7027
      0
      24
      84.80
      1990.50
      No
      1.0
      1.0
      1.0
      1.0
      0.0
      ...
      0.0
      1.0
      1.0
      0.0
      1.0
      0.0
      0.0
      1.0
      0
      No
    

    

      7028
      0
      72
      103.20
      7362.90
      No
      0.0
      1.0
      1.0
      1.0
      0.0
      ...
      0.0
      1.0
      1.0
      0.0
      1.0
      1.0
      0.0
      0.0
      0
      No
    

    

      7029
      0
      11
      29.60
      346.45
      No
      0.0
      1.0
      1.0
      0.0
      1.0
      ...
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      1.0
      0.0
      0
      No
    

    

      7030
      1
      4
      74.40
      306.60
      Yes
      1.0
      1.0
      0.0
      1.0
      0.0
      ...
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      0.0
      1.0
      1
      Yes
    

    

      7031
      0
      66
      105.65
      6844.50
      No
      1.0
      0.0
      0.0
      1.0
      0.0
      ...
      0.0
      1.0
      0.0
      1.0
      1.0
      0.0
      0.0
      0.0
      0
      No
    

  



7032 rows × 33 columns


























In [42]:



     


# Applying label encoding to the target variable
le = LabelEncoder()
Complete_set['Churn'] = le.fit_transform(Complete_set['Churn'])



     



















In [43]:



     


# Dropping irrelevant columns
Complete_set.drop(columns = ['remainder__Churn'], inplace = True)
Complete_set



     






















    
    
Out[43]:











  
    

      
      SeniorCitizen
      tenure
      MonthlyCharges
      TotalCharges
      Churn
      onehotencoder__gender_Male
      onehotencoder__Partner_Yes
      onehotencoder__Dependents_Yes
      onehotencoder__PhoneService_Yes
      onehotencoder__MultipleLines_No phone service
      ...
      onehotencoder__StreamingTV_Yes
      onehotencoder__StreamingMovies_No internet service
      onehotencoder__StreamingMovies_Yes
      onehotencoder__Contract_One year
      onehotencoder__Contract_Two year
      onehotencoder__PaperlessBilling_Yes
      onehotencoder__PaymentMethod_Credit card (automatic)
      onehotencoder__PaymentMethod_Electronic check
      onehotencoder__PaymentMethod_Mailed check
      remainder__SeniorCitizen
    

  
  
    

      0
      0
      1
      29.85
      29.85
      0
      0.0
      1.0
      0.0
      0.0
      1.0
      ...
      0.0
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      1.0
      0.0
      0
    

    

      1
      0
      34
      56.95
      1889.50
      0
      1.0
      0.0
      0.0
      1.0
      0.0
      ...
      0.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
      1.0
      0
    

    

      2
      0
      2
      53.85
      108.15
      1
      1.0
      0.0
      0.0
      1.0
      0.0
      ...
      0.0
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      0.0
      1.0
      0
    

    

      3
      0
      45
      42.30
      1840.75
      0
      1.0
      0.0
      0.0
      0.0
      1.0
      ...
      0.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0
    

    

      4
      0
      2
      70.70
      151.65
      1
      0.0
      0.0
      0.0
      1.0
      0.0
      ...
      0.0
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      1.0
      0.0
      0
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      7027
      0
      24
      84.80
      1990.50
      0
      1.0
      1.0
      1.0
      1.0
      0.0
      ...
      1.0
      0.0
      1.0
      1.0
      0.0
      1.0
      0.0
      0.0
      1.0
      0
    

    

      7028
      0
      72
      103.20
      7362.90
      0
      0.0
      1.0
      1.0
      1.0
      0.0
      ...
      1.0
      0.0
      1.0
      1.0
      0.0
      1.0
      1.0
      0.0
      0.0
      0
    

    

      7029
      0
      11
      29.60
      346.45
      0
      0.0
      1.0
      1.0
      0.0
      1.0
      ...
      0.0
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      1.0
      0.0
      0
    

    

      7030
      1
      4
      74.40
      306.60
      1
      1.0
      1.0
      0.0
      1.0
      0.0
      ...
      0.0
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      0.0
      1.0
      1
    

    

      7031
      0
      66
      105.65
      6844.50
      0
      1.0
      0.0
      0.0
      1.0
      0.0
      ...
      1.0
      0.0
      1.0
      0.0
      1.0
      1.0
      0.0
      0.0
      0.0
      0
    

  



7032 rows × 32 columns






























Splitting Data for Modeling¶


















In [44]:



     


# Splitting data
# Defining the target & predictor variables
X = Complete_set.drop(columns = ["Churn"])
y = Complete_set["Churn"]

# Splitting the dataframe into train and test
X_train, X_test, y_train, y_test= train_test_split(X, y, test_size = 0.25, random_state = 24, stratify = y)



     



















In [45]:



     


# Joining train dataset for modelling 
train_data = X_train.join(y_train, on = X_train.index)
train_data.head()



     






















    
    
Out[45]:











  
    

      
      SeniorCitizen
      tenure
      MonthlyCharges
      TotalCharges
      onehotencoder__gender_Male
      onehotencoder__Partner_Yes
      onehotencoder__Dependents_Yes
      onehotencoder__PhoneService_Yes
      onehotencoder__MultipleLines_No phone service
      onehotencoder__MultipleLines_Yes
      ...
      onehotencoder__StreamingMovies_No internet service
      onehotencoder__StreamingMovies_Yes
      onehotencoder__Contract_One year
      onehotencoder__Contract_Two year
      onehotencoder__PaperlessBilling_Yes
      onehotencoder__PaymentMethod_Credit card (automatic)
      onehotencoder__PaymentMethod_Electronic check
      onehotencoder__PaymentMethod_Mailed check
      remainder__SeniorCitizen
      Churn
    

  
  
    

      3996
      0
      38
      20.30
      743.05
      0.0
      0.0
      0.0
      1.0
      0.0
      0.0
      ...
      1.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      1.0
      0
      0
    

    

      3726
      0
      23
      77.15
      1759.40
      0.0
      1.0
      1.0
      1.0
      0.0
      1.0
      ...
      0.0
      0.0
      0.0
      1.0
      1.0
      0.0
      0.0
      0.0
      0
      0
    

    

      3246
      1
      37
      106.75
      4056.75
      0.0
      0.0
      0.0
      1.0
      0.0
      1.0
      ...
      0.0
      1.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      1
      1
    

    

      260
      1
      3
      41.15
      132.20
      1.0
      0.0
      0.0
      0.0
      1.0
      0.0
      ...
      0.0
      1.0
      0.0
      0.0
      1.0
      0.0
      1.0
      0.0
      1
      1
    

    

      4377
      0
      21
      20.35
      422.70
      1.0
      0.0
      0.0
      1.0
      0.0
      0.0
      ...
      1.0
      0.0
      1.0
      0.0
      1.0
      0.0
      0.0
      1.0
      0
      0
    

  



5 rows × 32 columns


























In [46]:



     


train_data.shape



     






















    
    
Out[46]:








(5274, 32)
























In [47]:



     


# Joining test dataset for modelling
test_data = X_test.join(y_test, on = X_test.index)
test_data.head()



     






















    
    
Out[47]:











  
    

      
      SeniorCitizen
      tenure
      MonthlyCharges
      TotalCharges
      onehotencoder__gender_Male
      onehotencoder__Partner_Yes
      onehotencoder__Dependents_Yes
      onehotencoder__PhoneService_Yes
      onehotencoder__MultipleLines_No phone service
      onehotencoder__MultipleLines_Yes
      ...
      onehotencoder__StreamingMovies_No internet service
      onehotencoder__StreamingMovies_Yes
      onehotencoder__Contract_One year
      onehotencoder__Contract_Two year
      onehotencoder__PaperlessBilling_Yes
      onehotencoder__PaymentMethod_Credit card (automatic)
      onehotencoder__PaymentMethod_Electronic check
      onehotencoder__PaymentMethod_Mailed check
      remainder__SeniorCitizen
      Churn
    

  
  
    

      4712
      1
      64
      102.10
      6688.10
      1.0
      0.0
      0.0
      1.0
      0.0
      1.0
      ...
      0.0
      1.0
      1.0
      0.0
      0.0
      0.0
      1.0
      0.0
      1
      0
    

    

      5934
      0
      1
      84.85
      84.85
      1.0
      0.0
      0.0
      1.0
      0.0
      0.0
      ...
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      1.0
      0.0
      0
      1
    

    

      6163
      0
      10
      70.10
      659.65
      1.0
      1.0
      0.0
      1.0
      0.0
      0.0
      ...
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      1.0
      0.0
      0
      1
    

    

      5758
      0
      65
      59.80
      3808.20
      0.0
      1.0
      0.0
      1.0
      0.0
      0.0
      ...
      0.0
      0.0
      0.0
      1.0
      1.0
      1.0
      0.0
      0.0
      0
      0
    

    

      212
      0
      61
      19.75
      1124.20
      1.0
      0.0
      1.0
      1.0
      0.0
      0.0
      ...
      1.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      1.0
      0
      0
    

  



5 rows × 32 columns


























In [48]:



     


test_data.shape



     






















    
    
Out[48]:








(1758, 32)
























In [49]:



     


# Applying a count to know the number of churned and not churned customers 
count_not_churned, count_churned = train_data['Churn'].value_counts()
count_not_churned, count_churned



     






















    
    
Out[49]:








(3872, 1402)
























In [50]:



     


count_churned



     






















    
    
Out[50]:








1402




























Over Sampling SMOTE Balancing¶


















In [51]:



     


# Performing splits to balance the data
X = train_data.drop('Churn',axis='columns')
y = train_data['Churn']



     



















In [52]:



     


# Using the SMOTE balancing to balance data
smote = SMOTE(sampling_strategy='minority')
X_sm, y_sm = smote.fit_resample(X, y)
y_sm.value_counts()



     






















    
    
Out[52]:








0    3872
1    3872
Name: Churn, dtype: int64
























In [53]:



     


# Further splitting of balanced data
X_train_sm, X_test_sm, y_train_sm, y_test_sm = train_test_split(X_sm, y_sm, test_size=0.2,random_state=15, stratify=y_sm)



     



















In [54]:



     


#Scaling the train_data set 
columns_to_scale = ['tenure','MonthlyCharges','TotalCharges']
scaler = MinMaxScaler()
train_data[columns_to_scale] = scaler.fit_transform(train_data[columns_to_scale])



     



















In [55]:



     


#Scaling the test_data set 
columns_to_scale = ['tenure','MonthlyCharges','TotalCharges']
scaler = MinMaxScaler()
test_data[columns_to_scale] = scaler.fit_transform(test_data[columns_to_scale])



     























Logistic Regression Model¶


















In [56]:



     


# Logistic Regression
log_reg = LogisticRegression()
log_reg_model = log_reg.fit(X_train_sm, y_train_sm)
warnings.filterwarnings('ignore')



     






















    
    






/Users/Admin/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning:

lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression

























In [57]:



     


## Feature Importance of the Random Forest Model
log_reg_importance = log_reg_model.coef_[0]
log_reg_importance = pd.DataFrame(log_reg_importance, index = X.columns)
log_reg_importance.reset_index(inplace = True)
log_reg_importance.rename(columns = {"index": "Feature", 0:"Score"}, inplace = True)
log_reg_importance.sort_values(by = "Score", ascending = False, inplace = True)
log_reg_importance



     






















    
    
Out[57]:











  
    

      
      Feature
      Score
    

  
  
    

      8
      onehotencoder__MultipleLines_No phone service
      0.773764
    

    

      30
      remainder__SeniorCitizen
      0.563929
    

    

      26
      onehotencoder__PaperlessBilling_Yes
      0.501136
    

    

      10
      onehotencoder__InternetService_Fiber optic
      0.480647
    

    

      28
      onehotencoder__PaymentMethod_Electronic check
      0.418456
    

    

      21
      onehotencoder__StreamingTV_Yes
      0.156438
    

    

      9
      onehotencoder__MultipleLines_Yes
      0.130062
    

    

      23
      onehotencoder__StreamingMovies_Yes
      0.118922
    

    

      4
      onehotencoder__gender_Male
      0.090496
    

    

      29
      onehotencoder__PaymentMethod_Mailed check
      0.026592
    

    

      2
      MonthlyCharges
      0.015397
    

    

      3
      TotalCharges
      0.000488
    

    

      5
      onehotencoder__Partner_Yes
      -0.012596
    

    

      27
      onehotencoder__PaymentMethod_Credit card (auto...
      -0.020909
    

    

      20
      onehotencoder__StreamingTV_No internet service
      -0.055022
    

    

      18
      onehotencoder__TechSupport_No internet service
      -0.055022
    

    

      11
      onehotencoder__InternetService_No
      -0.055022
    

    

      16
      onehotencoder__DeviceProtection_No internet se...
      -0.055022
    

    

      14
      onehotencoder__OnlineBackup_No internet service
      -0.055022
    

    

      12
      onehotencoder__OnlineSecurity_No internet service
      -0.055022
    

    

      22
      onehotencoder__StreamingMovies_No internet ser...
      -0.055022
    

    

      1
      tenure
      -0.077803
    

    

      6
      onehotencoder__Dependents_Yes
      -0.163997
    

    

      15
      onehotencoder__OnlineBackup_Yes
      -0.225331
    

    

      17
      onehotencoder__DeviceProtection_Yes
      -0.283030
    

    

      24
      onehotencoder__Contract_One year
      -0.478223
    

    

      7
      onehotencoder__PhoneService_Yes
      -0.532319
    

    

      19
      onehotencoder__TechSupport_Yes
      -0.767221
    

    

      13
      onehotencoder__OnlineSecurity_Yes
      -0.798669
    

    

      25
      onehotencoder__Contract_Two year
      -0.814633
    

    

      0
      SeniorCitizen
      -1.036961
    

  



























In [58]:



     


y_pred = log_reg.predict(X_test_sm)
y_true = y_test_sm



     



















In [59]:



     


# Confusion Matrix for Logistic regression model 
cm_lr = confusion_matrix(y_true,y_pred)
f, ax = plt.subplots(figsize=(8,8))
sns.heatmap(cm_lr, annot=True, linewidth=0.5, fmt=".0f",cmap='RdPu',ax = ax)
plt.xlabel = ('y_pred')
plt.ylabel = ('y_true')
plt.show()



     






















    
    































In [60]:



     


# Evaluating the model
smote_report = classification_report(y_true, y_pred, target_names= ["Stayed", "Churned"])
print(smote_report)



     






















    
    






              precision    recall  f1-score   support

      Stayed       0.78      0.78      0.78       775
     Churned       0.78      0.79      0.78       774

    accuracy                           0.78      1549
   macro avg       0.78      0.78      0.78      1549
weighted avg       0.78      0.78      0.78      1549

























In [61]:



     


# Finding the F2 Score
fbeta_score(y_true, y_pred, average='binary', beta= 0.9)



     






















    
    
Out[61]:








0.781624216941063




























We have the accuracy score for the Logistic regression model to be 0.78. Also the F1 and F2 scores are 0.78 and 0.781 respectively¶






















Random Forest with Smote Balance¶


















In [62]:



     


# Specifying the Model name and setting default parameters 
rfc = RandomForestClassifier(random_state=101)
rfc = rfc.fit(X_train_sm, y_train_sm)



     



















In [63]:



     


# Feature Importance of the Random Forest Model
rf_importance = rfc.feature_importances_
rf_importance = pd.DataFrame(rf_importance, columns = ["score"]).reset_index()
rf_importance["Feature"] = list(X.columns)
rf_importance.drop(columns = ["index"], inplace = True)
rf_importance.sort_values(by = "score", ascending = False, ignore_index = True, inplace = True)



     



















In [64]:



     


## Visualizing the feature importances
fig = px.bar(rf_importance, x = "Feature", y = "score")
fig.show()



     






















    
    







                            
                    
























In [65]:



     


rfc_pred = rfc.predict(X_test_sm)
rfc_true = y_test_sm



     



















In [66]:



     


# Confusion matrix for RandomForest classifier
cm_rfc = confusion_matrix(rfc_true,rfc_pred)
f, ax = plt.subplots(figsize=(8,8))
sns.heatmap(cm_rfc, annot=True, linewidth=0.5, fmt=".0f",cmap='RdPu',ax = ax)
plt.xlabel = ('rfc_pred')
plt.ylabel = ('rfc_true')
plt.show()



     






















    
    































In [67]:



     


# Evaluating the model
smote_report = classification_report(rfc_true, rfc_pred, target_names= ["Stayed", "Churned"])
print(smote_report)



     






















    
    






              precision    recall  f1-score   support

      Stayed       0.85      0.91      0.88       775
     Churned       0.90      0.84      0.87       774

    accuracy                           0.87      1549
   macro avg       0.88      0.87      0.87      1549
weighted avg       0.88      0.87      0.87      1549

























In [68]:



     


fbeta_score(rfc_true, rfc_pred, average='binary', beta= 0.9)



     






















    
    
Out[68]:








0.872068642467322




























The accuracy score for the RandomForest Classifier is 0.87. This Model also has an F1 and F2 score of 0.87 and 0.872 respectively. The random forest classifier gives a better accuracy as compared to the Logistic regression¶






















Decision Tree Classifier with Smote Balance¶


















In [69]:



     


# Specifying the Model name and setting default parameters 
dt = DecisionTreeClassifier(random_state=100)
dt = dt.fit(X_train_sm, y_train_sm)



     



















In [70]:



     


dt_pred = dt.predict(X_test_sm)
dt_true = y_test_sm



     



















In [71]:



     


#Confusion Matrix for Decision Tree Classifier 
cm_dt = confusion_matrix(dt_true,dt_pred)
f, ax = plt.subplots(figsize=(8,8))
sns.heatmap(cm_dt, annot=True, linewidth=0.5, fmt=".0f",cmap='RdPu',ax = ax)
plt.xlabel = ('dt_pred')
plt.ylabel = ('dt_true')
plt.show()



     






















    
    































In [72]:



     


# Evaluating the model
smote_report = classification_report(dt_true, dt_pred, target_names= ["Stayed", "Churned"])
print(smote_report)



     






















    
    






              precision    recall  f1-score   support

      Stayed       0.81      0.83      0.82       775
     Churned       0.83      0.80      0.82       774

    accuracy                           0.82      1549
   macro avg       0.82      0.82      0.82      1549
weighted avg       0.82      0.82      0.82      1549

























In [73]:



     


fbeta_score(dt_true, dt_pred, average='binary', beta= 0.9)



     






















    
    
Out[73]:








0.8183447755344934




























The accuracy score for the Decision Tree Classifier is 0.80. This Model also has an F1 and F2 score of 0.80 and 0.802 respectively. This model outworks the Logistic regression model but gives a lower accuracy as compared to the Randome Forest classifier¶






















K-Nearest Neighbours¶


















In [74]:



     


# Specifying the Model name and setting default parameters 
knn = KNeighborsClassifier(n_neighbors = 15)
knn = knn.fit(X_train_sm, y_train_sm)



     



















In [75]:



     


knn_pred = knn.predict(X_test_sm)
knn_true = y_test_sm



     



















In [76]:



     


# Confusion Matrix for KNN
cm_knn = confusion_matrix(knn_true,knn_pred)
f, ax = plt.subplots(figsize=(8,8))
sns.heatmap(cm_knn, annot=True, linewidth=0.5, fmt=".0f",cmap='RdPu',ax = ax)
plt.xlabel = ('knn_pred')
plt.ylabel = ('knn_true')
plt.show()



     






















    
    































In [77]:



     


# Evaluating the model
smote_report = classification_report(knn_true, knn_pred, target_names= ["Stayed", "Churned"])
print(smote_report)



     






















    
    






              precision    recall  f1-score   support

      Stayed       0.78      0.71      0.75       775
     Churned       0.74      0.80      0.77       774

    accuracy                           0.76      1549
   macro avg       0.76      0.76      0.76      1549
weighted avg       0.76      0.76      0.76      1549

























In [78]:



     


fbeta_score(knn_true, knn_pred, average='binary', beta= 0.9)



     






















    
    
Out[78]:








0.7643352750281749




























Stochastic Gradient Descent¶


















In [79]:



     


# Specifying the model name and setting default parameters
sgd = SGDClassifier(loss= 'modified_huber', shuffle= True, random_state = 101)
sgd = sgd.fit(X_train_sm, y_train_sm)



     



















In [80]:



     


sgd_pred = sgd.predict(X_test_sm)
sgd_true = y_test_sm



     



















In [81]:



     


# Confusion matrix for Stochastic Gradient Descent Model 
cm_sgd = confusion_matrix(sgd_true,sgd_pred)
f, ax = plt.subplots(figsize=(8,8))
sns.heatmap(cm_sgd, annot=True, linewidth=0.5, fmt=".0f",cmap='RdPu',ax = ax)
plt.xlabel = ('sgd_pred')
plt.ylabel = ('sgd_true')
plt.show()



     






















    
    































In [82]:



     


# Evaluating the model
smote_report = classification_report(sgd_true, sgd_pred, target_names= ["Stayed", "Churned"])
print(smote_report)



     






















    
    






              precision    recall  f1-score   support

      Stayed       0.67      0.86      0.76       775
     Churned       0.81      0.58      0.68       774

    accuracy                           0.72      1549
   macro avg       0.74      0.72      0.72      1549
weighted avg       0.74      0.72      0.72      1549

























In [83]:



     


fbeta_score(sgd_true, sgd_pred, average='binary', beta= 0.9)



     






















    
    
Out[83]:








0.6872213841381766




























Hyperparameter Tuning & Cross Validation for RandomForest Model¶






















Using RandomizedSearch¶


















In [84]:



     


# Defining the values and setting the grid to be used in the RandomizedSearch
random_grid = {
    "n_estimators":list(range(10, 101, 10)),
    "max_depth": [1, 5, 10, 20, 50, 75, 100, 150, 200],
    "bootstrap": [True, False],
    "criterion": ["gini", "entropy"],
    "max_features": ["sqrt", "log2", None],
    "random_state": [24]
}



     



















In [85]:



     


# Running the RandomizedSearch Cross-Validation with the grid
rf_rscv = RandomizedSearchCV(estimator= rfc,
                             param_distributions= random_grid,
                             n_iter= 30,
                             cv= 10,
                             random_state= 24,
                             n_jobs= 5)
rf_rscv.fit(X_train_sm,y_train_sm)
warnings.filterwarnings('ignore')



     



















In [87]:



     


# Looking at the best combination of hyperparameters for the model
best_params = rf_rscv.best_params_
print("The best combination of hyperparameters for the model will be:")

for param_name in sorted(best_params.keys()):
    print(f"{param_name}: {best_params[param_name]}")



     






















    
    






The best combination of hyperparameters for the model will be:
bootstrap: True
criterion: gini
max_depth: 10
max_features: log2
n_estimators: 80
random_state: 24
























In [88]:



     


# Viewing the best parameters after cross validation
best_params



     






















    
    
Out[88]:








{'random_state': 24,
 'n_estimators': 80,
 'max_features': 'log2',
 'max_depth': 10,
 'criterion': 'gini',
 'bootstrap': True}
























In [89]:



     


# Defining the best version of the model with the best parameters
best_rf_model = RandomForestClassifier(random_state= 24,
                                       n_estimators = 80,
                                       max_features = 'log2',
                                       max_depth = 10,
                                       criterion = 'gini',
                                       bootstrap = True)



     



















In [90]:



     


# Discovering the best score for the Random forest cross validation 
rf_rscv.best_score_



     






















    
    
Out[90]:








0.8590807233310752
























In [91]:



     


rf_rscv.score(X_train_sm,y_train_sm)



     






















    
    
Out[91]:








0.9075060532687651
























In [92]:



     


# Defining the best version of the model with the best parameters
best_rf_model = RandomForestClassifier(random_state= 24,
                                       n_estimators = 80,
                                       max_features = 'log2',
                                       max_depth = 10,
                                       criterion = 'gini',
                                       bootstrap = True)



     



















In [93]:



     


# Fitting the model to the training data
best_rf_model = best_rf_model.fit(X_train_sm,y_train_sm)



     



















In [94]:



     


# Predicting the test data
best_rf_pred = best_rf_model.predict(X_test_sm)



     



















In [95]:



     


# Evaluating the model
best_rf_report = classification_report(y_test_sm, best_rf_pred, target_names=["Stayed", "Churned"])
print(best_rf_report)



     






















    
    






              precision    recall  f1-score   support

      Stayed       0.87      0.88      0.87       775
     Churned       0.88      0.87      0.87       774

    accuracy                           0.87      1549
   macro avg       0.87      0.87      0.87      1549
weighted avg       0.87      0.87      0.87      1549

























In [96]:



     


# Calculating the accuracy score
accuracy = accuracy_score(y_test_sm, best_rf_pred)
accuracy = "{:.5f}".format(accuracy)
print("Accuracy:", accuracy)



     






















    
    






Accuracy: 0.87282
























In [97]:



     


# Calculating the F2 Score
f2_score = fbeta_score(y_test_sm, best_rf_pred, beta= 0.5)
f2_score = "{:.5f}".format(f2_score)
print("F2 Score:", f2_score)



     






















    
    






F2 Score: 0.87454
























In [98]:



     


# Confusion Matrix
best_rf_conf_mat = confusion_matrix(y_test_sm, best_rf_pred)
best_rf_conf_mat = (pd.DataFrame(best_rf_conf_mat).reset_index(drop=True)).rename(columns={0: "Stayed", 1: "Churned"})
best_rf_conf_mat



     






















    
    
Out[98]:











  
    

      
      Stayed
      Churned
    

  
  
    

      0
      680
      95
    

    

      1
      102
      672
    

  



























In [99]:



     


# Visualizing the Confusion Matrix
f, ax = plt.subplots()
sns.heatmap(best_rf_conf_mat,
            annot= True,
            linewidth= 1.0,
            fmt= ".0f",
            cmap= "RdPu",
            ax= ax)
plt.show()



     






















    
    































In [100]:



     


# Cross-validating the scores of the best model
final_cv_score = cross_val_score(best_rf_model,
                                 X_train_sm, y_train_sm,
                                 cv= 10).mean()
final_cv_score = "{0:.5}".format(final_cv_score)
print("Average score of the model on cross-validation is:", final_cv_score)



     






















    
    






Average score of the model on cross-validation is: 0.85908




























Using GridSearch CV¶


















In [101]:



     


# Defining the values and setting the grid to be used in the GridSearch
gscv_param_grid = {
    "n_estimators": list(range(50, 400, 20)),
    "max_features": ["sqrt", "log2"],
    "max_depth": [50, 100, 150, 200, 300],
    "criterion": ["gini", "entropy"],
    "random_state": [24],
    "bootstrap": [True],
}



     



















In [102]:



     


# Running the GridSearch Cross-Validation with the specified grid
rf_gscv = GridSearchCV(estimator= rfc,
                       param_grid= gscv_param_grid,
                       n_jobs= -1,
                       cv= 10
                      )



     



















In [103]:



     


# Fitting the model to the training data
rf_gscv.fit(X_train_sm, y_train_sm)



     






















    
    
Out[103]:








GridSearchCV(cv=10, estimator=RandomForestClassifier(random_state=101),
             n_jobs=-1,
             param_grid={'bootstrap': [True], 'criterion': ['gini', 'entropy'],
                         'max_depth': [50, 100, 150, 200, 300],
                         'max_features': ['sqrt', 'log2'],
                         'n_estimators': [50, 70, 90, 110, 130, 150, 170, 190,
                                          210, 230, 250, 270, 290, 310, 330,
                                          350, 370, 390],
                         'random_state': [24]})
























In [104]:



     


# Looking at the best combination of hyperparameters for the model
best_params = rf_gscv.best_params_
print("The best combination of hyperparameters for the model will be:")

for param_name in sorted(best_params.keys()):
    print(f"{param_name}: {best_params[param_name]}")



     






















    
    






The best combination of hyperparameters for the model will be:
bootstrap: True
criterion: entropy
max_depth: 50
max_features: log2
n_estimators: 390
random_state: 24
























In [105]:



     


# Viewing the best parameters after cross validation 
best_params



     






















    
    
Out[105]:








{'bootstrap': True,
 'criterion': 'entropy',
 'max_depth': 50,
 'max_features': 'log2',
 'n_estimators': 390,
 'random_state': 24}
























In [106]:



     


# Defining the best version of the model with the best parameters
best_grid_rf_model = RandomForestClassifier(random_state= 24,
                                       n_estimators = 390,
                                       max_features = 'log2',
                                       max_depth = 50,
                                       criterion = 'entropy',
                                       bootstrap = True)



     



















In [107]:



     


# Discovering the best score for the Random forest cross validation 
rf_gscv.best_score_



     






















    
    
Out[107]:








0.8598874355099276
























In [108]:



     


rf_gscv.score(X_train_sm,y_train_sm)



     






















    
    
Out[108]:








0.9988700564971752
























In [109]:



     


# Defining the best version of the model with the best parameters
best_grid_rf_model = RandomForestClassifier(random_state= 24,
                                       n_estimators = 390,
                                       max_features = 'log2',
                                       max_depth = 50,
                                       criterion = 'entropy',
                                       bootstrap = True)



     



















In [110]:



     


# Fitting the model to the training data
best_grid_rf_model = best_grid_rf_model.fit(X_train_sm,y_train_sm)



     



















In [111]:



     


# Predicting the test data
best_grid_rf_pred = best_grid_rf_model.predict(X_test_sm)



     



















In [112]:



     


# Evaluating the model
best_grid_rf_report = classification_report(y_test_sm, best_grid_rf_pred, target_names=["Stayed", "Churned"])
print(best_grid_rf_report)



     






















    
    






              precision    recall  f1-score   support

      Stayed       0.85      0.91      0.88       775
     Churned       0.90      0.84      0.87       774

    accuracy                           0.87      1549
   macro avg       0.88      0.87      0.87      1549
weighted avg       0.88      0.87      0.87      1549

























In [113]:



     


# Calculating the accuracy score
accuracy = accuracy_score(y_test_sm, best_grid_rf_pred)
accuracy = "{:.5f}".format(accuracy)
print("Accuracy:", accuracy)



     






















    
    






Accuracy: 0.87411
























In [114]:



     


# Calculating the F2 Score
f2_score = fbeta_score(y_test_sm, best_grid_rf_pred, beta= 0.5)
f2_score = "{:.5f}".format(f2_score)
print("F2 Score:", f2_score)



     






















    
    






F2 Score: 0.88904
























In [115]:



     


# Confusion Matrix
best_grid_rf_cm = confusion_matrix(y_test_sm, best_grid_rf_pred)
best_grid_rf_cm = (pd.DataFrame(best_grid_rf_cm).reset_index(drop=True)).rename(columns={0: "Stayed", 1: "Churned"})
best_grid_rf_cm



     






















    
    
Out[115]:











  
    

      
      Stayed
      Churned
    

  
  
    

      0
      705
      70
    

    

      1
      125
      649
    

  



























In [116]:



     


# Visualizing the Confusion Matrix
f, ax = plt.subplots()
sns.heatmap(best_grid_rf_cm,
            annot= True,
            linewidth= 1.0,
            fmt= ".0f",
            cmap= "RdPu",
            ax= ax)
plt.show()



     






















    
    



































Future Prediction¶






















RandomForest Classifier¶


















In [117]:



     


# Defining the target & predictor variables
test_X = test_data.drop(columns=["Churn"])
test_y = test_data["Churn"]



     



















In [118]:



     


# Balancing the test data using SMOTE
smote2 = SMOTE(sampling_strategy='minority')
X_test_sm, y_test_sm = smote.fit_resample(test_X, test_y)
y_sm.value_counts()



     






















    
    
Out[118]:








0    3872
1    3872
Name: Churn, dtype: int64
























In [119]:



     


# Predicting the test data
sm_pred = best_grid_rf_model.predict(X_test_sm)



     



















In [120]:



     


# Evaluating the model
smtest_report = classification_report(y_test_sm,sm_pred, target_names=["Stayed","Churned"])
print(smtest_report)



     






















    
    






              precision    recall  f1-score   support

      Stayed       0.83      0.72      0.77      1291
     Churned       0.75      0.85      0.80      1291

    accuracy                           0.79      2582
   macro avg       0.79      0.79      0.78      2582
weighted avg       0.79      0.79      0.78      2582

























In [121]:



     


# Calculating the F2 Score
f2_score = fbeta_score(y_test_sm,sm_pred, beta=0.5)
f2_score = "{:.5f}".format(f2_score)
print("F2 Score:", f2_score)



     






















    
    






F2 Score: 0.77025
























In [122]:



     


# Confusion Matrix
conf_mat = confusion_matrix(y_test_sm,sm_pred)
conf_mat = (pd.DataFrame(conf_mat).reset_index(
    drop=True)).rename(columns={0: "Stayed", 1: "Churned"})
conf_mat



     






















    
    
Out[122]:











  
    

      
      Stayed
      Churned
    

  
  
    

      0
      928
      363
    

    

      1
      190
      1101
    

  



























In [123]:



     


# Visualizing the Confusion Matrix
f, ax = plt.subplots()
sns.heatmap(conf_mat,
            annot=True,
            linewidth=1.0,
            fmt=".0f",
            cmap="RdPu",
            ax=ax)
plt.show()